PART I: Prepare and clean the data

First, install OWSLib package that will enable getting ZIP code (in German: PLZ) geodata from Berlin Open Data, a data portal provided by the local govt in Berlin

In [3]:
#conda install -c conda-forge owslib
In [4]:
# import key libraries
import numpy as np
import pandas as pd
import requests
import geocoder
from owslib.wfs import WebFeatureService
# import beautiful soup library for scraping
from bs4 import BeautifulSoup

German postcodes with coordinates are available here: https://www.wiwald.com/ds/kostenlose-liste-deutscher-postleitzahlen-und-zugehoeriger-orte/id/ww-german-postal-codes. I have downloaded a json file and will convert it into a pandas dataframe.

In [66]:
df_plz=pd.read_json('ww-german-postal-codes.json', orient='index')
df_plz.head()
Out[66]:
primary_key zipcode city state community latitude longitude
1 1 1945 Grünewald Brandenburg Landkreis Oberspreewald-Lausitz 51.4000 14.0000
2 2 1945 Lindenau Brandenburg Landkreis Oberspreewald-Lausitz 51.4000 13.7333
3 3 1945 Hohenbocka Brandenburg Landkreis Oberspreewald-Lausitz 51.4310 14.0098
4 4 1945 Schwarzbach Brandenburg Landkreis Oberspreewald-Lausitz 51.4500 13.9333
5 5 1945 Guteborn Brandenburg Landkreis Oberspreewald-Lausitz 51.4167 13.9333

Now, this is for whole Germany - we are only interested in Berlin, so we remove all the tip codes (PLZ in German) for other towns

In [265]:
df_plz=df_plz.loc[df_plz['state'] == 'Berlin']
# df_plz.head()
df_plz.head()
Out[265]:
primary_key zipcode city state community latitude longitude
1288 1288 10115 Berlin Berlin Berlin, Stadt 52.5323 13.3846
1289 1289 10117 Berlin Berlin Berlin, Stadt 52.5170 13.3872
1290 1290 10119 Berlin Berlin Berlin, Stadt 52.5305 13.4053
1291 1291 10178 Berlin Berlin Berlin, Stadt 52.5213 13.4096
1292 1292 10179 Berlin Berlin Berlin, Stadt 52.5122 13.4164

Now we would like to add 2 things to this dataframe: 1- The population per PLZ, and 2- Neighborhood per PLZ. these are available from the local statistics agency in Berlin: https://www.statistik-berlin-brandenburg.de/publikationen/Stat_Berichte/2018/SB_A01-05-00_2017h02_BE.xlsx. I have made a csv from the relevant worksheet.

In [93]:
df_einw=pd.read_csv('PLZ_Kiez_Einwohner.csv', delimiter =';', sep=';')
df_einw.head()
#df_einw.shape
Out[93]:
PLZ Kiez Einwohner
0 10115 Mitte 26274
1 10117 Mitte 15531
2 10119 Mitte 15064
3 10119 Pankow 4606
4 10178 Friedrichsh.-Kreuzb. 81

Now, as there are instances of more than one neighborhood (in German: Kiez) per PLZ, as for example PLZ 10119 above, we aggregate that information in the rows: Each row shows one PLZ with its population and the corresponding neighborhood(s)

In [96]:
df_einw_clean=df_einw.groupby(['PLZ']).agg({'Einwohner':'sum', 'Kiez':'sum'})
df_einw_clean.head(5)
#df_einw_clean.sum(axis=0)
#df_einw_clean.shape
Out[96]:
Einwohner Kiez
PLZ
10115 26274 Mitte
10117 15531 Mitte
10119 19670 MittePankow
10178 14466 Friedrichsh.-Kreuzb.Mitte
10179 23970 Friedrichsh.-Kreuzb.Mitte

Now that we have the population and the geodata, we would be interested in the purchasing power per neighborhood, ideally per PLZ code. There is a webpage of a German newspaper, Bild, showing that info as a table. Although the information is older (2013), the relative purchasing power across neighborhoods is quite stable so we should be able to use it for our purposes. Here's the link: https://www.bild.de/regional/berlin/einkommen/kaufkraft-liste-berliner-kieze-28373092.bild.html . We can scrape the tables using Beautiful Soup.

In [114]:
page = requests.get('https://www.bild.de/regional/berlin/einkommen/kaufkraft-liste-berliner-kieze-28373092.bild.html')
soup = BeautifulSoup(page.content, 'html.parser')
table = soup.find_all('table')
df_income=pd.DataFrame()
for i in range(12):
    data=pd.read_html(str(table))[i]
    df_income=df_income.append(data)
#df_income = pd.read_html(str(table))[11]
df_income.head()
Out[114]:
PLZ, Lage Kaufkraft pro Haushalt* Rang**
0 10585 Deutsche Oper​ 3015 Euro 45
1 10587 TU/Otto-Suhr-Allee​ 2753 Euro 81
2 10589 Mierendorffplatz​ 2718 Euro 90
3 10623 Savignyplatz​ 3545 Euro 15
4 10625 Karl-August-Platz​ 3048 Euro 40
In [115]:
df_income
Out[115]:
PLZ, Lage Kaufkraft pro Haushalt* Rang**
0 10585 Deutsche Oper​ 3015 Euro 45
1 10587 TU/Otto-Suhr-Allee​ 2753 Euro 81
2 10589 Mierendorffplatz​ 2718 Euro 90
3 10623 Savignyplatz​ 3545 Euro 15
4 10625 Karl-August-Platz​ 3048 Euro 40
... ... ... ...
10 12557 Wendenschloß​ 2477 Euro 133
11 12559 Müggelheim​ 2490 Euro 130
12 12587 Friedrichshagen​ 2405 Euro 147
13 12589 Rahnsdorf ​ 3164 Euro 30
14 Bezirk 2378 Euro NaN

202 rows × 3 columns

Doesn't look bad, but we have to clean it a little, as PLZ is shown with location information, each neighboprhood has its average value showing up as duplicates and the income figure is shown with string "Euro". Let's clean this up:

In [118]:
df_income.drop_duplicates(subset=['PLZ, Lage'], keep=False, inplace=True)
df_income.shape
Out[118]:
(190, 3)
In [737]:
new = df_income['PLZ, Lage'].str.split(" ", n = 1, expand = True)
new2 = df_income['Kaufkraft pro Haushalt*'].str.split(n = 0, expand = True)
df_income['PLZ']=new[0]
df_income['income']=new2[0]
df_income.head()
Out[737]:
PLZ, Lage Kaufkraft pro Haushalt* Rang** PLZ Kaufkraft income
0 10585 Deutsche Oper​ 3015 Euro 45 10585 3015 3015
1 10587 TU/Otto-Suhr-Allee​ 2753 Euro 81 10587 2753 2753
2 10589 Mierendorffplatz​ 2718 Euro 90 10589 2718 2718
3 10623 Savignyplatz​ 3545 Euro 15 10623 3545 3545
4 10625 Karl-August-Platz​ 3048 Euro 40 10625 3048 3048

Now 2 more things to get before we start our cluster analysis: 1- Political leanings per PLZ / neighborhood and 2- Number of coffee shops per neighborhood / PLZ. Plitical leanings. Results of the last election in Berlin are available under https://www.govdata.de/web/guest/daten/-/details/wahlen-in-berlin-2016-abgeordnetenhaus-und-bezirksverordnetenversammlungen-endgultiges-ergebnis . As the election regions are not identical with PLZ, I had to add a PLZ column into the data, using the mapping available here: https://www.wahlen-berlin.de/wahlen/BE2016/Wahlkreiseinteil/wahlkreiseinteil.asp. I saved the csv - let's see how it looks:

In [183]:
df_wahlen = pd.read_csv('Berlin_AH16_W1.csv', delimiter =';', sep=';')
df_wahlen.shape
Out[183]:
(2432, 62)

Already looking good - but we have to clean up a bit: Postal votes cannot be mapped to PLZ so we have to get rid of them. Then we need to group the data by PLZ (the original df has more than 2000 rows, each row representing an election location.

In [188]:
df_wahlen.dropna(subset=['PLZ'], inplace=True)
In [189]:
df_wahlen.head()
Out[189]:
Adresse Stimmart Bezirksnummer Bezirksname Wahlbezirk Wahlbezirksart PLZ Abgeordneten-\nhauswahlkreis Bundestags-\nwahlkreis Berlin\nOstWest ... EB 9 EB 10 EB 11 EB 12 EB 13 EB 14 EB 15 EB 16 EB 17 EB 18
37 01W100 Erststimme 1 Mitte 100 Urnenwahlbezirk 10557.0 1 75 W ... 0 0 0 0 0 0 0 0 0 0
38 01W101 Erststimme 1 Mitte 101 Urnenwahlbezirk 10115.0 1 75 O ... 0 0 0 0 0 0 0 0 0 0
39 01W102 Erststimme 1 Mitte 102 Urnenwahlbezirk 10115.0 1 75 O ... 0 0 0 0 0 0 0 0 0 0
40 01W103 Erststimme 1 Mitte 103 Urnenwahlbezirk 10115.0 1 75 O ... 0 0 0 0 0 0 0 0 0 0
41 01W104 Erststimme 1 Mitte 104 Urnenwahlbezirk 10115.0 1 75 O ... 0 0 0 0 0 0 0 0 0 0

5 rows × 62 columns

In [190]:
df_wahlen=df_wahlen.astype({'PLZ': int})
df_wahlen.head()
Out[190]:
Adresse Stimmart Bezirksnummer Bezirksname Wahlbezirk Wahlbezirksart PLZ Abgeordneten-\nhauswahlkreis Bundestags-\nwahlkreis Berlin\nOstWest ... EB 9 EB 10 EB 11 EB 12 EB 13 EB 14 EB 15 EB 16 EB 17 EB 18
37 01W100 Erststimme 1 Mitte 100 Urnenwahlbezirk 10557 1 75 W ... 0 0 0 0 0 0 0 0 0 0
38 01W101 Erststimme 1 Mitte 101 Urnenwahlbezirk 10115 1 75 O ... 0 0 0 0 0 0 0 0 0 0
39 01W102 Erststimme 1 Mitte 102 Urnenwahlbezirk 10115 1 75 O ... 0 0 0 0 0 0 0 0 0 0
40 01W103 Erststimme 1 Mitte 103 Urnenwahlbezirk 10115 1 75 O ... 0 0 0 0 0 0 0 0 0 0
41 01W104 Erststimme 1 Mitte 104 Urnenwahlbezirk 10115 1 75 O ... 0 0 0 0 0 0 0 0 0 0

5 rows × 62 columns

In [194]:
df_wahlen.columns
Out[194]:
Index(['Adresse', 'Stimmart', 'Bezirksnummer', 'Bezirksname', 'Wahlbezirk',
       'Wahlbezirksart', 'PLZ', 'Abgeordneten-\nhauswahlkreis',
       'Bundestags-\nwahlkreis', 'Berlin\nOstWest',
       'Wahlberechtigte insgesamt', 'Wahlberechtigte A1', 'Wahlberechtigte A2',
       'Wahlberechtigte A3', 'Wähler', 'Wähler B1', 'Ungültige Stimmen',
       'Gültige Stimmen', 'SPD', 'CDU', 'GRÜNE', 'DIE LINKE', 'PIRATEN', 'NPD',
       'FDP', 'Tierschutzpartei', 'pro Deutschland', 'Die PARTEI', 'BIG',
       'DKP', 'ödp', 'PSG', 'BüSo', 'B', 'DL', 'ALFA', 'Tierschutzallianz',
       'AfD', 'DIE EINHEIT', 'DIE VIOLETTEN', 'Graue Panther',
       'MENSCHLICHE WELT', 'MIETERPARTEI', 'Gesundheits-\nforschung', 'EB 1',
       'EB 2', 'EB 3', 'EB 4', 'EB 5', 'EB 6', 'EB 7', 'EB 8', 'EB 9', 'EB 10',
       'EB 11', 'EB 12', 'EB 13', 'EB 14', 'EB 15', 'EB 16', 'EB 17', 'EB 18'],
      dtype='object')
In [210]:
df_wahlen=df_wahlen[['Bezirksname', 'PLZ', 'Gültige Stimmen', 'SPD', 'CDU', 'GRÜNE', 'DIE LINKE',
                             'FDP', 'AfD']]
In [211]:
df_wahlen.head()
Out[211]:
Bezirksname PLZ Gültige Stimmen SPD CDU GRÜNE DIE LINKE FDP AfD
37 Mitte 10557 461 129 83 88 75 13 59
38 Mitte 10115 707 169 143 178 83 57 58
39 Mitte 10115 566 130 112 131 65 58 60
40 Mitte 10115 635 140 92 192 92 61 38
41 Mitte 10115 600 148 99 167 66 66 37

So what we did here is we kept the columns with the main parties as well as total number of valid votes. SPD = social democrats, CDU= conservatives , Grüne = greens, linke = socialists, fdp= liberals, afd= far right. Let's group by PLZ to get what we need: Percentages per each party per PLZ.

In [212]:
df_wahlen_clean=df_wahlen.groupby(['PLZ']).agg({'Bezirksname':'max', 'Gültige Stimmen':'sum', 'SPD':'sum'
                                               ,'CDU':'sum', 'GRÜNE':'sum', 'DIE LINKE':'sum', 'FDP':'sum',
                                               'AfD':'sum'})
df_wahlen_clean['SPD']=df_wahlen_clean['SPD']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean['CDU']=df_wahlen_clean['CDU']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean['GRÜNE']=df_wahlen_clean['GRÜNE']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean['DIE LINKE']=df_wahlen_clean['DIE LINKE']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean['FDP']=df_wahlen_clean['FDP']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean['AfD']=df_wahlen_clean['AfD']/df_wahlen_clean['Gültige Stimmen']
df_wahlen_clean.head()
Out[212]:
Bezirksname Gültige Stimmen SPD CDU GRÜNE DIE LINKE FDP AfD
PLZ
10115 Mitte 2958 0.244760 0.168357 0.262677 0.125085 0.090264 0.078431
10117 Mitte 8971 0.230521 0.156170 0.177461 0.216475 0.072902 0.113700
10119 Pankow 3563 0.213865 0.122930 0.336514 0.174011 0.074937 0.044906
10178 Mitte 1544 0.253238 0.120466 0.102332 0.312824 0.040803 0.139896
10179 Mitte 7142 0.259311 0.113974 0.145477 0.279194 0.041025 0.125175

Now we're ready to get the final variable for our analysis: number of cafés per PLZ. For this, we're going to use the Foursquare API.

In [235]:
import json
In [278]:
CLIENT_ID = 'XDP1E1K13BDFE051QJR1RGQSQITXQOFY2FC2KR4ECKTO5CGX'
CLIENT_SECRET = 'TOJPKLGKIKNDEOUEWMABC3OHCUPDTD5ACKLTRWI4C5SUPYYA' 
VERSION = '20180605' 
LIMIT = 100 # we want 100 results per PLZ
radius = 500 # we want to limit the radius from PLZ centre to 500m
categoryId = '4bf58dd8d48988d16d941735' # 4bf58dd8d48988d1e0931735' # IDs for café & coffee shop
In [280]:
def getNearbyVenues(names, latitudes, longitudes, radius=radius, categoryId=categoryId):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        # print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}&categoryId={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT,
            categoryId)
            
        # make the GET request
        results = requests.get(url).json()
        
        # return only relevant information for each nearby venue
        venues_list.append(results)
    
    return(venues_list)

Now let's get the coffee shops per PLZ from foursquare:

In [281]:
berlin_cafes=getNearbyVenues(names=df_plz['zipcode'],
                                   latitudes=df_plz['latitude'],
                                   longitudes=df_plz['longitude']
                                  )
berlin_cafes
Out[281]:
[{'meta': {'code': 200, 'requestId': '5fb16e69334c670565cb8409'},
  'response': {'headerLocation': 'Mitte',
   'headerFullLocation': 'Mitte, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 15,
   'suggestedBounds': {'ne': {'lat': 52.5368000045, 'lng': 13.39198368973932},
    'sw': {'lat': 52.5277999955, 'lng': 13.377216310260682}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': [{'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4fd0f671e4b0044b25a40009',
        'name': 'Oslo Kaffebar',
        'location': {'address': 'Eichendorffstr. 13',
         'crossStreet': 'Invalidenstr.',
         'lat': 52.53102915837446,
         'lng': 13.386889100074768,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53102915837446,
           'lng': 13.386889100074768}],
         'distance': 209,
         'postalCode': '10115',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Eichendorffstr. 13 (Invalidenstr.)',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4fd0f671e4b0044b25a40009-0'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4b15069ef964a520eaa723e3',
        'name': 'Café Bondi',
        'location': {'address': 'Eichendorffstr. 5',
         'lat': 52.530319398010086,
         'lng': 13.387454917590597,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.530319398010086,
           'lng': 13.387454917590597}],
         'distance': 293,
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Eichendorffstr. 5', 'Berlin', 'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4b15069ef964a520eaa723e3-1'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4b5760d7f964a520a23428e3',
        'name': 'Brasserie La Bonne Franquette',
        'location': {'address': 'Chausseestr. 110',
         'crossStreet': 'Invalidenstr.',
         'lat': 52.53110165668639,
         'lng': 13.382489160805864,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53110165668639,
           'lng': 13.382489160805864}],
         'distance': 195,
         'postalCode': '10115',
         'cc': 'DE',
         'neighborhood': 'Mitte',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Chausseestr. 110 (Invalidenstr.)',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []},
        'venuePage': {'id': '57552622'}},
       'referralId': 'e-0-4b5760d7f964a520a23428e3-2'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '5d81fa2a153d7800087d5c4c',
        'name': 'Espresso House',
        'location': {'lat': 52.53066,
         'lng': 13.383123,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53066,
           'lng': 13.383123}],
         'distance': 208,
         'cc': 'DE',
         'country': 'Deutschland',
         'formattedAddress': ['Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-5d81fa2a153d7800087d5c4c-3'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '577638e1498e545ee0d166f8',
        'name': '19grams',
        'location': {'address': 'Chausseestr. 36',
         'lat': 52.53303691027667,
         'lng': 13.380054366842362,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53303691027667,
           'lng': 13.380054366842362}],
         'distance': 318,
         'postalCode': '10115',
         'cc': 'DE',
         'neighborhood': 'Mitte',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Chausseestr. 36',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-577638e1498e545ee0d166f8-4'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '58b55329fc73d402335a0e73',
        'name': 'R/D',
        'location': {'address': 'Chausseestr. 19',
         'lat': 52.5302338636797,
         'lng': 13.383697271347046,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.5302338636797,
           'lng': 13.383697271347046}],
         'distance': 237,
         'postalCode': '10115',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Chausseestr. 19',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []},
        'venuePage': {'id': '486971060'}},
       'referralId': 'e-0-58b55329fc73d402335a0e73-5'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4fcfbbe7e4b087291e62d8f2',
        'name': 'Karaca',
        'location': {'address': 'Chausseestr. 106',
         'lat': 52.53172205686423,
         'lng': 13.381594054043058,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53172205686423,
           'lng': 13.381594054043058}],
         'distance': 213,
         'postalCode': '10115',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Chausseestr. 106',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
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           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-57ac57bc498ed153471494a0-15'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '5127e7bee4b03a531599160b',
        'name': 'Gameduell Coffeemaker',
        'location': {'address': 'Taubenstrasse 24',
         'lat': 52.51385670505711,
         'lng': 13.39547288000693,
         'labeledLatLngs': [{'label': 'display',
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           'lng': 13.39547288000693}],
         'distance': 465,
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Taubenstrasse 24', 'Berlin', 'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
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          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-5127e7bee4b03a531599160b-16'},
      {'reasons': {'count': 0,
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          'type': 'general',
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       'venue': {'id': '4cfa405134c1a09353033f0e',
        'name': 'Bäcker Wiedemann',
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         'lng': 13.40459257961874,
         'labeledLatLngs': [{'label': 'display',
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           'lng': 13.40459257961874}],
         'distance': 470,
         'postalCode': '10178',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Karl-Liebknecht-Str. 5',
          '10178 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
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       'venue': {'id': '4b7fcd07f964a5205d3e30e3',
        'name': 'the coffee shop',
        'location': {'address': 'Hausvogteiplatz 13',
         'crossStreet': 'Niederwallstr.',
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         'distance': 474,
         'postalCode': '10117',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Hausvogteiplatz 13 (Niederwallstr.)',
          '10117 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
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       'referralId': 'e-0-4b7fcd07f964a5205d3e30e3-18'},
      {'reasons': {'count': 0,
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          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '572881d6498e8d9e56d37742',
        'name': 'Café & Restaurant Spreeblick',
        'location': {'address': 'Propststr. 9',
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         'lng': 13.405863046646118,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.516152310515814,
           'lng': 13.405863046646118}],
         'distance': 408,
         'postalCode': '10178',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Propststr. 9', '10178 Berlin', 'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []},
        'venuePage': {'id': '184575676'}},
       'referralId': 'e-0-572881d6498e8d9e56d37742-19'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '5b699b2ee96d0c002c832626',
        'name': 'Allegretto Gran Caffè',
        'location': {'address': 'Anna-Louisa-Karsch-Str. 2',
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         'labeledLatLngs': [{'label': 'display',
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           'lng': 13.401193910758593}],
         'distance': 413,
         'postalCode': '10178',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Anna-Louisa-Karsch-Str. 2',
          '10178 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-5b699b2ee96d0c002c832626-20'}]}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb6a614367fd9d084e7'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Zehlendorf',
   'headerFullLocation': 'Zehlendorf, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 2,
   'suggestedBounds': {'ne': {'lat': 52.4413000045, 'lng': 13.245867677201286},
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       'venue': {'id': '4ba63547f964a520b23b39e3',
        'name': 'Café Krone',
        'location': {'address': 'Argentinische Allee 2',
         'crossStreet': 'Limastr.',
         'lat': 52.4381810599327,
         'lng': 13.233061821513736,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.4381810599327,
           'lng': 13.233061821513736}],
         'distance': 399,
         'postalCode': '14163',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
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         'formattedAddress': ['Argentinische Allee 2 (Limastr.)',
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          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
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           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4ba63547f964a520b23b39e3-0'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '51f52d0d498e29a9db4ac659',
        'name': 'La Piazza',
        'location': {'address': 'Bülowstraße 1–8A',
         'lat': 52.437458314435176,
         'lng': 13.23329327316576,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.437458314435176,
           'lng': 13.23329327316576}],
         'distance': 360,
         'postalCode': '14163',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Bülowstraße 1–8A',
          '14163 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-51f52d0d498e29a9db4ac659-1'}]}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb7c699df34c85cf8e8'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Zehlendorf',
   'headerFullLocation': 'Zehlendorf, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4220000045, 'lng': 13.260964452074623},
    'sw': {'lat': 52.412999995499995, 'lng': 13.246235547925378}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': []}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb7c656a10ca6a86d14'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Drakestraße',
   'headerFullLocation': 'Drakestraße, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4257000045, 'lng': 13.28386507007963},
    'sw': {'lat': 52.4166999955, 'lng': 13.269134929920371}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': []}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb73e071546b3af5c8c'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Onkel Toms Hütte',
   'headerFullLocation': 'Onkel Toms Hütte, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4541000045, 'lng': 13.26466981816516},
    'sw': {'lat': 52.445099995499994, 'lng': 13.249930181834841}},
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     'name': 'recommended',
     'items': []}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb8b8c07577f77c0040'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Grunewald',
   'headerFullLocation': 'Grunewald, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4876000045, 'lng': 13.243875429106657},
    'sw': {'lat': 52.4785999955, 'lng': 13.229124570893342}},
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     'name': 'recommended',
     'items': []}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb8c699df34c85cfe14'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Dahlem',
   'headerFullLocation': 'Dahlem, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4634000045, 'lng': 13.290271374720646},
    'sw': {'lat': 52.4543999955, 'lng': 13.275528625279353}},
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     'name': 'recommended',
     'items': []}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb8a614367fd9d08cf8'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Wilmersdorf',
   'headerFullLocation': 'Wilmersdorf, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 2,
   'suggestedBounds': {'ne': {'lat': 52.4779000045, 'lng': 13.319173803308106},
    'sw': {'lat': 52.468899995499996, 'lng': 13.304426196691894}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': [{'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '55e177b1498e7d11eed25f18',
        'name': 'Lotte am Platz',
        'location': {'address': 'Rüdesheimer Platz 1',
         'lat': 52.473191999250005,
         'lng': 13.316996424945875,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.473191999250005,
           'lng': 13.316996424945875}],
         'distance': 353,
         'postalCode': '14197',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Rüdesheimer Platz 1',
          '14197 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-55e177b1498e7d11eed25f18-0'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4c76146bff1fb60c1214f8a7',
        'name': 'BioBackHaus',
        'location': {'address': 'Rüdesheimer Str. 8',
         'lat': 52.47294020777343,
         'lng': 13.314430976945431,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.47294020777343,
           'lng': 13.314430976945431}],
         'distance': 185,
         'postalCode': '14197',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Rüdesheimer Str. 8',
          '14197 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4c76146bff1fb60c1214f8a7-1'}]}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb9e998a41dc43b86b7'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Schmargendorf',
   'headerFullLocation': 'Schmargendorf, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 2,
   'suggestedBounds': {'ne': {'lat': 52.4822000045, 'lng': 13.302474523908396},
    'sw': {'lat': 52.473199995499996, 'lng': 13.287725476091603}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': [{'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '51a8f2f6498ef7983d47d58a',
        'name': 'Looms',
        'location': {'address': 'Berkaer Str. 40',
         'crossStreet': 'Kösener Str.',
         'lat': 52.47706417072388,
         'lng': 13.288147842437526,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.47706417072388,
           'lng': 13.288147842437526}],
         'distance': 476,
         'postalCode': '14199',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Berkaer Str. 40 (Kösener Str.)',
          '14199 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-51a8f2f6498ef7983d47d58a-0'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4c5ae0c6d3aee21e09bb6b55',
        'name': 'Coffee Oase',
        'location': {'address': 'Breite Str. 32',
         'lat': 52.47481020871978,
         'lng': 13.292175887527808,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.47481020871978,
           'lng': 13.292175887527808}],
         'distance': 377,
         'postalCode': '14199',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Breite Str. 32',
          '14199 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d16d941735',
          'name': 'Café',
          'pluralName': 'Cafés',
          'shortName': 'Café',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/cafe_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4c5ae0c6d3aee21e09bb6b55-1'}]}]}},
 {'meta': {'code': 200, 'requestId': '5fb16eb9e2d2433c21b89fdb'},
  'response': {'warning': {'text': "There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area."},
   'headerLocation': 'Müggelheim',
   'headerFullLocation': 'Müggelheim, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'cafe',
   'totalResults': 0,
   'suggestedBounds': {'ne': {'lat': 52.4045000045, 'lng': 13.72406153089859},
    'sw': {'lat': 52.395499995499996, 'lng': 13.709338469101409}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': []}]}}]
In [283]:
#berlin_coffeshops=getNearbyVenues(names=df_plz['zipcode'],
#                                   latitudes=df_plz['latitude'],
#                                   longitudes=df_plz['longitude'],
#                                   categoryId='4bf58dd8d48988d1e0931735'
#                                  )
berlin_coffeshops
Out[283]:
[{'meta': {'code': 200, 'requestId': '5fb16f41dfad1e14df23ba00'},
  'response': {'headerLocation': 'Mitte',
   'headerFullLocation': 'Mitte, Berlin',
   'headerLocationGranularity': 'neighborhood',
   'query': 'coffee shop',
   'totalResults': 11,
   'suggestedBounds': {'ne': {'lat': 52.5368000045, 'lng': 13.39198368973932},
    'sw': {'lat': 52.5277999955, 'lng': 13.377216310260682}},
   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': [{'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '4fd0f671e4b0044b25a40009',
        'name': 'Oslo Kaffebar',
        'location': {'address': 'Eichendorffstr. 13',
         'crossStreet': 'Invalidenstr.',
         'lat': 52.53102915837446,
         'lng': 13.386889100074768,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.53102915837446,
           'lng': 13.386889100074768}],
         'distance': 209,
         'postalCode': '10115',
         'cc': 'DE',
         'city': 'Berlin',
         'state': 'Berlin',
         'country': 'Deutschland',
         'formattedAddress': ['Eichendorffstr. 13 (Invalidenstr.)',
          '10115 Berlin',
          'Deutschland']},
        'categories': [{'id': '4bf58dd8d48988d1e0931735',
          'name': 'Coffee Shop',
          'pluralName': 'Coffee Shops',
          'shortName': 'Coffee Shop',
          'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/coffeeshop_',
           'suffix': '.png'},
          'primary': True}],
        'photos': {'count': 0, 'groups': []}},
       'referralId': 'e-0-4fd0f671e4b0044b25a40009-0'},
      {'reasons': {'count': 0,
        'items': [{'summary': 'This spot is popular',
          'type': 'general',
          'reasonName': 'globalInteractionReason'}]},
       'venue': {'id': '58b55329fc73d402335a0e73',
        'name': 'R/D',
        'location': {'address': 'Chausseestr. 19',
         'lat': 52.5302338636797,
         'lng': 13.383697271347046,
         'labeledLatLngs': [{'label': 'display',
           'lat': 52.5302338636797,
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   'groups': [{'type': 'Recommended Places',
     'name': 'recommended',
     'items': []}]}}]

Now that we got the JSON files, let's put them into a pd dataframe:

In [485]:
berlin_cafes[0]['response']['groups'][0]['items'][2]['venue']['name']
Out[485]:
'Brasserie La Bonne Franquette'
In [465]:
berlin_cafes[0]['response']['groups'][0]['items'][2]['venue']['location']['postalCode']
Out[465]:
'10115'
In [492]:
cafes=[]
plz_cafes=[]
for i in range (195):
    for j in range (50):
        try:
            cafes.append(berlin_cafes[i]['response']['groups'][0]['items'][j]['venue']['name'])
        except:
            cafes.append('NaN')

for i in range (195):
    for j in range (50):
        try:
            plz_cafes.append(berlin_cafes[i]['response']['groups'][0]['items'][j]['venue']['location']['postalCode'])
        except:
            plz_cafes.append('NaN')            

cafe_dict={'PLZ':plz_cafes,'Venue':cafes}
In [494]:
df_cafe=pd.DataFrame.from_dict(cafe_dict)
df_cafe.head()
Out[494]:
PLZ Venue
0 10115 Oslo Kaffebar
1 NaN Café Bondi
2 10115 Brasserie La Bonne Franquette
3 NaN Espresso House
4 10115 19grams
In [505]:
coffeeshops=[]
plz_coffeeshops=[]
for i in range (195):
    for j in range (50):
        try:
            coffeeshops.append(berlin_coffeshops[i]['response']['groups'][0]['items'][j]['venue']['name'])
        except:
            coffeeshops.append('NaN')

for i in range (195):
    for j in range (50):
        try:
            plz_coffeeshops.append(berlin_coffeshops[i]['response']['groups'][0]['items'][j]['venue']['location']['postalCode'])
        except:
            plz_coffeeshops.append('NaN')            

coffeeshop_dict={'PLZ':plz_coffeeshops,'Venue':coffeeshops}
In [508]:
df_coffeeshop=pd.DataFrame.from_dict(coffeeshop_dict)
df_coffeeshop.head()
Out[508]:
PLZ Venue
0 10115 Oslo Kaffebar
1 10115 R/D
2 10115 19grams
3 10115 The Barn
4 NaN Café Bondi
In [530]:
df_cafes_berlin=pd.concat([df_cafe, df_coffeeshop])
In [535]:
df_cafes_berlin=df_cafes_berlin[df_cafes_berlin.PLZ!='NaN']
In [612]:
df_cafes_berlin=df_cafes_berlin.astype({'PLZ':int})
In [613]:
df_cafes_number=df_cafes_berlin.groupby(['PLZ']).agg({'Venue':'count'})
df_cafes_number.head()
Out[613]:
Venue
PLZ
10115 21
10117 69
10119 68
10178 103
10179 20

Now we have x key dataframes: 1- df_plz 2-df_income 3-df_wahlen_clean 4-df_cafes_number and 5 df_einw_clean for population. We now need to merge them based on PLZ.

In [841]:
df_plz_clean=df_plz[['zipcode', 'latitude', 'longitude']]
df_plz_clean=df_plz_clean.rename(columns={'zipcode':'PLZ'})
df_plz_clean=df_plz_clean.set_index('PLZ')
df_income_clean=df_income[['PLZ', 'income']]
df_income_clean=df_income_clean.astype({'PLZ':int})
df_income_clean=df_income_clean.set_index('PLZ')
In [842]:
df=df_plz_clean.join(df_income_clean) # joined geodata with income figures
In [843]:
df=df.join(df_einw_clean) # joined with population figures
In [844]:
df_wahlen_clean=df_wahlen_clean[['SPD', 'CDU', 'GRÜNE', 'DIE LINKE', 'FDP', 'AfD']]
df=df.join(df_wahlen_clean) # joined with resp. political party % votes
In [845]:
df=df.join(df_cafes_number) # join with number of cafes
In [846]:
df=df.rename(columns={'Einwohner':'population', 'Venue':'number_of_cafes'})
In [855]:
for i in range(len(df['income'])):
    if df.iloc[i,2]=='k.A.':
        df.iloc[i,2]= 2696
In [853]:
# we now need to clean up the income column & convert to float
import math
x=float('nan')
i=0
while i < (len(df['income'])):
    try: 
        if math.isnan(df.iloc[i,2]) == True:
            df.iloc[i,2]= 2696
        else:
            df.iloc[i,2]=df.iloc[i,2]
        i+=1
    except TypeError:
        i+=1
        continue
In [856]:
df=df.astype({'income': float})
In [849]:
df.dropna(subset=['population'], inplace=True)

Now let's replace NaN values in number of cafes with 0s (assuming if foursquare doesn't show them, they're not there!)

In [857]:
for i in range(len(df['number_of_cafes'])):
    try: 
        if math.isnan(df.iloc[i,11])==True:
            df.iloc[i,11]=0
    except TypeError:
        continue
In [858]:
# replace the NaN values in the party vote columns with party averages for the whole of Berlin:
SPD = 0.248
CDU = 0.198
GRÜNE = 0.158
LINKE = 0.154
AFD = 0.141
FDP = 0.056

for i in range(len(df['SPD'])):
    try: 
        if math.isnan(df.iloc[i,5])==True:
            df.iloc[i,5]=SPD
            df.iloc[i,6]=CDU
            df.iloc[i,7]=GRÜNE
            df.iloc[i,8]=LINKE
            df.iloc[i,9]=FDP
            df.iloc[i,10]=AFD
    except TypeError:
        continue
In [859]:
df.head(20)
Out[859]:
latitude longitude income population Kiez SPD CDU GRÜNE DIE LINKE FDP AfD number_of_cafes
PLZ
10115 52.5323 13.3846 3118.0 26274.0 Mitte 0.244760 0.168357 0.262677 0.125085 0.090264 0.078431 21.0
10117 52.5170 13.3872 3673.0 15531.0 Mitte 0.230521 0.156170 0.177461 0.216475 0.072902 0.113700 69.0
10119 52.5305 13.4053 3018.0 19670.0 MittePankow 0.213865 0.122930 0.336514 0.174011 0.074937 0.044906 68.0
10178 52.5213 13.4096 2717.0 14466.0 Friedrichsh.-Kreuzb.Mitte 0.253238 0.120466 0.102332 0.312824 0.040803 0.139896 103.0
10179 52.5122 13.4164 2583.0 23970.0 Friedrichsh.-Kreuzb.Mitte 0.259311 0.113974 0.145477 0.279194 0.041025 0.125175 20.0
10243 52.5123 13.4394 2428.0 30655.0 Friedrichsh.-Kreuzb. 0.185139 0.080490 0.259760 0.245244 0.031075 0.087722 6.0
10245 52.5007 13.4647 2439.0 33509.0 Friedrichsh.-Kreuzb. 0.171369 0.077022 0.312147 0.216711 0.035244 0.067221 6.0
10247 52.5161 13.4656 2463.0 39491.0 Friedrichsh.-Kreuzb.Pankow 0.156329 0.065162 0.327305 0.221043 0.025407 0.058736 25.0
10249 52.5238 13.4428 2436.0 28885.0 Friedrichsh.-Kreuzb.Pankow 0.205592 0.092083 0.210302 0.274578 0.037836 0.112293 6.0
10315 52.5132 13.5148 2151.0 33424.0 Lichtenberg 0.227868 0.111517 0.071133 0.309392 0.024862 0.204617 4.0
10317 52.4979 13.4908 2342.0 23027.0 Friedrichsh.-Kreuzb.Lichtenberg 0.222026 0.102065 0.189577 0.270600 0.034022 0.121731 3.0
10318 52.4835 13.5287 2690.0 27217.0 Lichtenberg 0.269375 0.160280 0.101124 0.258043 0.033228 0.145587 8.0
10319 52.4992 13.5188 2104.0 24481.0 Lichtenberg 0.248000 0.198000 0.158000 0.154000 0.056000 0.141000 0.0
10365 52.5206 13.4969 2278.0 27052.0 Lichtenberg 0.213463 0.092585 0.106927 0.321659 0.026829 0.185659 0.0
10367 52.5246 13.4821 2192.0 21735.0 Lichtenberg 0.225220 0.113551 0.136763 0.274153 0.039523 0.156211 1.0
10369 52.5295 13.4695 2286.0 20386.0 Lichtenberg 0.234551 0.102902 0.112843 0.260881 0.030091 0.196131 4.0
10405 52.5352 13.4257 2773.0 32065.0 Pankow 0.257936 0.110728 0.245075 0.208185 0.046244 0.067992 39.0
10407 52.5336 13.4492 2555.0 25254.0 Pankow 0.180162 0.097166 0.281377 0.232794 0.046559 0.087045 4.0
10409 52.5443 13.4414 1942.0 23033.0 Pankow 0.246957 0.085797 0.139130 0.263768 0.024348 0.153043 5.0
10435 52.5378 13.4112 2941.0 17400.0 MittePankow 0.244661 0.105743 0.308314 0.205681 0.062409 0.000000 53.0

GREAT! Now we have a cleaned-up dataset. We can start analyzing the data!

PART II: Run cluster analysis based on ZIP codes

We'll do K-means clustering here. To find the optimal number of clusters, we'll utilize the "elbow method". Before we do that, the relevant colums are income, population, party votes and number of cafes per PLZ. Let's select & standardize these.

In [860]:
from sklearn.preprocessing import StandardScaler
X = df[['income','population','SPD','CDU','GRÜNE','DIE LINKE','FDP','AfD','number_of_cafes']].values
scaler = StandardScaler().fit(X)
# Standardize the columns.
df_stan = df.copy()
X_stan = scaler.transform(X)
df_stan[['income','population','SPD','CDU','GRÜNE','DIE LINKE',
         'FDP','AfD','number_of_cafes']] = X_stan
df_stan.head()
Out[860]:
latitude longitude income population Kiez SPD CDU GRÜNE DIE LINKE FDP AfD number_of_cafes
PLZ
10115 52.5323 13.3846 0.795168 0.952178 Mitte -0.210863 -0.235586 1.075738 -0.303655 1.138978 -0.982463 0.833516
10117 52.5170 13.3872 2.028190 -0.566072 Mitte -0.543981 -0.388277 0.120688 0.889122 0.565641 -0.430709 3.957918
10119 52.5305 13.4053 0.573001 0.018871 MittePankow -0.933630 -0.804735 1.903248 0.334899 0.632851 -1.506950 3.892826
10178 52.5213 13.4096 -0.095719 -0.716582 Friedrichsh.-Kreuzb.Mitte -0.012519 -0.835603 -0.721307 2.146605 -0.494324 -0.020876 6.171036
10179 52.5122 13.4164 -0.393421 0.626566 Friedrichsh.-Kreuzb.Mitte 0.129548 -0.916949 -0.237758 1.707682 -0.486999 -0.251184 0.768425

Now we can find out the ideal cluster number via elbow:

In [864]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
    kmeans.fit(X_stan)
    wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

So, K=4 seems to be a reasonable number for clusters, let's see what our analysis tells us:

In [972]:
kmeans = KMeans(n_clusters = 6, init = 'k-means++', random_state =  42)
y_kmeans = kmeans.fit_predict(X_stan)
In [973]:
y_kmeans
Out[973]:
array([1, 5, 5, 5, 4, 4, 4, 4, 4, 2, 4, 2, 4, 2, 4, 2, 4, 4, 4, 5, 5, 4,
       4, 1, 1, 1, 4, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,
       1, 1, 0, 0, 1, 1, 1, 1, 5, 1, 1, 5, 4, 5, 5, 4, 4, 5, 4, 4, 4, 4,
       3, 4, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 0, 3, 0, 3, 1, 3,
       3, 0, 0, 3, 3, 1, 3, 4, 3, 3, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 4, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 4, 2, 2,
       2, 2, 2, 2, 2, 2, 4, 4, 4, 1, 1, 4, 1, 4, 4, 3, 3, 3, 3, 1, 3, 3,
       0, 0, 3, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1,
       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=int32)
In [974]:
df['cluster'] = pd.Series(y_kmeans, index=df.index)
In [975]:
df.head()
Out[975]:
latitude longitude income population Kiez SPD CDU GRÜNE DIE LINKE FDP AfD number_of_cafes cluster PLZ
PLZ
10115 52.5323 13.3846 3118.0 26274.0 Mitte 0.244760 0.168357 0.262677 0.125085 0.090264 0.078431 21.0 1 10115
10117 52.5170 13.3872 3673.0 15531.0 Mitte 0.230521 0.156170 0.177461 0.216475 0.072902 0.113700 69.0 5 10117
10119 52.5305 13.4053 3018.0 19670.0 MittePankow 0.213865 0.122930 0.336514 0.174011 0.074937 0.044906 68.0 5 10119
10178 52.5213 13.4096 2717.0 14466.0 Friedrichsh.-Kreuzb.Mitte 0.253238 0.120466 0.102332 0.312824 0.040803 0.139896 103.0 5 10178
10179 52.5122 13.4164 2583.0 23970.0 Friedrichsh.-Kreuzb.Mitte 0.259311 0.113974 0.145477 0.279194 0.041025 0.125175 20.0 4 10179
Let's investigate the clusters:
In [976]:
df.groupby(['cluster']).mean()
Out[976]:
latitude longitude income population SPD CDU GRÜNE DIE LINKE FDP AfD number_of_cafes PLZ
cluster
0 52.484915 13.284307 3543.296296 14144.000000 0.250494 0.282043 0.164814 0.066754 0.105535 0.111677 6.962963 12753.370370
1 52.497458 13.344867 2763.250000 16075.187500 0.275193 0.175457 0.225062 0.120458 0.062646 0.101202 7.354167 11824.541667
2 52.513995 13.532224 2564.736842 21381.421053 0.222846 0.164129 0.072669 0.234640 0.023014 0.231119 1.263158 12515.368421
3 52.507223 13.318334 2623.857143 20346.542857 0.295699 0.259662 0.090003 0.072123 0.066926 0.181405 0.742857 13017.342857
4 52.515734 13.432853 2451.250000 25743.406250 0.230387 0.103602 0.225818 0.219064 0.034831 0.104523 10.437500 11581.406250
5 52.512290 13.413050 2837.600000 21002.100000 0.205435 0.088303 0.328085 0.215162 0.040880 0.048443 60.800000 10725.700000

The cluster analysis reveals some interesting insights: Cluster 0: High-income / conseravtive neighborhood with low population: The PLZ areas in this cluster are parts of town with detached houses with high-income households who tend to elect conservative / center-right. Could potentially work for a high-end cafe if the design & interior is a bit more on the traditional side. Cluster 1: Average-income "green" residential areas: Similar to cluster 0 in terms of population and coffe shop density, while the political opinion is a bit more tilted toward the green party and the income is in-line with city average. Probably equally attractive as cluster 0, but the clientele will be completely different - need more vegan stuff!! Cluster 2: Far-right / low income neighborhoods: Parts of town with low household income, tendency to elect far-right, very low number of coffe shops. Also high % for far-left. Former East-Berlin neighborhoods. Not interesting. Cluster 3: Mixed neighborhoods: These neighborhoods are quite heterogeneous in that there is a large social democratic crowd but also a high number of far-right people. Number of cafes quite low, so also not really interesting for our coffe shop idea. Cluster 4: "Poor, but sexy": Alternative cluster: This is interesting. Lower purchasing power, highly cosmopolitan, progressive and high-population. Probably student neighborhoods. Definitely interesting. Cluster 5: Affluent porgressive green neighbohoods: This is the ideal cluster for our idea. Former leftie / green students who now are affluent and love to spend time and money in alternative but expensive coffe shops!

PART III: Visualize results

Let's create a folium choropleth map with the PLZ / zip codes and their respective clusters:

In [984]:
df2=df
df2['PLZ']=df2.index
df2=df2.astype({'PLZ':str})
m = folium.Map(location=[52.52, 13.4], zoom_start=11)
folium.Choropleth(
    geo_data=plz_geo,
    data=df2,
    columns=['PLZ','cluster'],
    key_on='properties.plz',
    fill_color='Spectral',
    fill_opacity=0.7,
    line_opacity=0.5,
    reset=True
).add_to(m)

m
Out[984]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Awesome: Cluster 5 was the most interesting for us. Seems like the ideal spots for our coffee shop are in central part of former East-Berlin where affluent alternatives dominate, as well as in Kreuzberg. The green areas would be a second choice - so called "poor but sexy" alternative neighborhoods. We now have favorite locations for our new café!

Here's a bonus analysis: Income by zip code. Seems like richer parts of town elect conservative!

In [986]:
df2=df
df2['PLZ']=df2.index
df2=df2.astype({'PLZ':str})
m = folium.Map(location=[52.52, 13.4], zoom_start=11)
folium.Choropleth(
    geo_data=plz_geo,
    data=df2,
    columns=['PLZ','income'],
    key_on='properties.plz',
    fill_color='Blues',
    fill_opacity=0.7,
    line_opacity=0.5,
    reset=True
).add_to(m)

m
Out[986]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]:
In [ ]: